The type of workload managed by a computing entity, such as database management system, is a key consideration in tuning the computing entity. For example, allocation for resources, such as main memory, can vary significantly depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). It would be preferable for administrators of the computing entity, such as database administrators, to recognize significant shifts in workload types that would require reconfiguring the computing entity or co-locating similar workloads to maintain acceptable levels of performance.
Currently, the identification of a workload's type is performed by a person by pre-classifying a given workload into one of a plurality of predefined classes, for example, “test”, “web server”, “database”, where each of the classes identifies an expected load pattern and/or behavior. However, increasingly, human detection of workload type is becoming more difficult as the complexity of workloads increase. The increased workload complexity causes detection of workload patterns that are used to determine workload types to become more difficult. Accordingly, current workload identification technology is limited in its capabilities and suffers from at least the above constraints and deficiencies.